Vol.11, No.1, February 2022.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks

Nataliia Havlovska, Hanna Koptieva, Olena Babchynska, Yevhenii Rudnichenko, Viktor Lopatovskyi, Vadym Prytys

© 2022 Yevhenii Rudnichenko, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 11, Issue 1, Pages 13-23, ISSN 2217-8309, DOI: 10.18421/TEM111-02, February 2022.

Received: 22 June 2021.

Revised:   14 January 2022.
Accepted: 21 January 2022.
Published: 28 February 2022.

Abstract:

Enterprise management is based on the need to make and justify management decisions that contribute to its development. It is almost impossible to determine the risk of a particular managerial decision, and excessive risk in the implementation of individual projects can lead to loss of business. Therefore, management faces the need to find a balance between benefits and risks, at which, on the one hand, it will be possible to develop a company and, on the other hand, adhere to postulates of safetyoriented management. Since management decisions cannot be foreseen for all possible situations and combinations of risk-benefit ratios, a universal model is proposed. It implies a golden ratio, depending on the limited number of current conditions, that would satisfy an enterprise management from the standpoint of sufficient justification on a decision. The article proposes a probabilistic neural network architecture and Matlab parameters of a probabilistic neural network for diagnosing the states of a safety-oriented control system. The proposed model in the form of a probabilistic neural network generates a response to input data on previous month under estimation, and forms an optimal state for a next month.

Keywords –managerial decision, economic security, risk, benefit, neural network.

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